Ecometabolomics reveal physiological adaptations of Asiatic toads (Bufo gargarizans Cantor, 1842) to different environments along an altitudinal gradient
Bibliographic record
Abstract
Animals inhabiting altitudinal gradients exhibit a variety of adaptations to environmental variations. However, to date, changes in metabolomic profiles with altitude have been poorly characterized. Here, we used target and non-target metabolomic analyses of liver to investigate the environmental adaptations of Asiatic toads (Bufo gargarizans) along an altitudinal gradient (50 m, 1200 m, 2300 m, and 3400 m above sea level). Non-targeted metabolomics analysis identified 775 metabolites, and k-means clustering analysis showed that up-regulated metabolites along the altitudinal gradient were significantly enriched in the thiamine and sphingolipid metabolism pathways. Down-regulated metabolites were mainly enriched in alanine, aspartate and glutamate metabolism and glycolysis/glycogenesis. Weighted gene co-expression network analysis showed that metabolites positively correlated with altitude were mainly related to sphingolipid metabolism and glycerophospholipid metabolism, whereas those negatively correlated were involved in glycolysis/gluconeogenesis and glycerolipid metabolism. Moreover, a total of 52 metabolites were identified by the targeted metabolomics analysis. K-means clustering analysis showed that down-regulated metabolites along the altitudinal gradient were mainly enriched in pentose phosphate pathway and glycolysis/gluconeogenesis. In addition, toads from different altitudes exhibited significant variation in the activities of key metabolic enzymes, including phosphofructokinase, lactate dehydrogenase, and α-ketoglutarate dehydrogenase. In conclusion, the metabolic profiles of Asiatic toads along an altitudinal gradient differed significantly. These findings enhance our understanding of the physiological adaptations of toads to different environments along an altitudinal gradient.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".